How are edge features implemented in Geometric Deep Learning?
In the paper Neural Message Passing for Quantum Chemistry (2017), the authors (from Google, Google Brain and Google DeepMind) introduce a framework called message passing neural network (MPNN), which generalizes previously proposed geometric deep learning models. In section 2 of the paper, they describe this MPNN framework and they state that edge features can also be learned, using the MPNN framework, by introducing hidden states for all edges in the graph, $\mathbf{h}_{e_{vw}}^t$, where $t$ is the iteration number and $e_{vw}$ is the edge from node $v$ to node $w$.

See also the paper Machine learning prediction errors better than DFT accuracy, which is cited in the MPNN paper as an example where the authors also learn the edge features. In section 2 of the MPNN paper, they briefly describe this specific instantiation of the MPNN framework, called the Molecular Graph Convolutions.